import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#import both the GHG Emissions by Sector file and the Commodity Prices and Background Indicators file
Ghg = pd.read_excel("GHG Emissions by Sector.xlsx")
Ghg1 = pd.read_excel("GHG Emissions by Sector.xlsx")
CB = pd.read_excel("Commodity Prices and Background Indicators.xlsx")
CB1 = pd.read_excel("Commodity Prices and Background Indicators.xlsx")

#Ghg1 and CB1 will be used solely for the purpose of filtering and merging both of these dataframes
#The resulting dataframe is DF3

Ghg = Ghg.round(2) #Removed additional significant digits and limited it to 2 per value.
Ghg = Ghg.rename(columns={'Unnamed: 0': 'Source of GHG'}) #Added a title for column indicating each source
#Changed the names of values that had duplicate entries to better reflect the source of GHG.
Ghg.loc[2, 'Source of GHG'] = 'Space Heating - Residential'
Ghg.loc[3, 'Source of GHG'] = 'Water Heating - Residential'
Ghg.loc[7, 'Source of GHG'] = 'Lighting - Residential'
Ghg.loc[8, 'Source of GHG'] = 'Space Cooling - Residential'
Ghg.loc[10, 'Source of GHG'] = 'Space Heating - Commercial'
Ghg.loc[11, 'Source of GHG'] = 'Water Heating - Commercial'
Ghg.loc[14, 'Source of GHG'] = 'Lighting - Commercial'
Ghg.loc[15, 'Source of GHG'] = 'Space Cooling - Commercial'
Ghg.loc[31, 'Source of GHG'] = 'Light Trucks - Passenger'
Ghg.loc[34, 'Source of GHG'] = 'Air - Passenger'
Ghg.loc[35, 'Source of GHG'] = 'Rail - Passenger'
Ghg.loc[37, 'Source of GHG'] = 'Light Trucks - Freight'
Ghg.loc[40, 'Source of GHG'] = 'Air - Freight'
Ghg.loc[41, 'Source of GHG'] = 'Rail - Freight'
#removed subheadings which had total values of GHG per sector to better focus our dataset.
#Ghg.drop([0, 1, 9, 17, 28, 29, 36], axis=0, inplace=True)

#dropped initial row which showed total GHG Emissions to focus on each sector specifically.
CB.drop([0], axis=0, inplace=True)
CB = CB.round(2)
CB = CB.rename(columns={'Unnamed: 0': 'Source of GHG'})
CB

#this will pull the rows consisting of totals for each sector from our Ghg dataset.
Ghg1 = Ghg1.iloc[[9, 17, 44]]
Ghg1 = Ghg1.rename(columns={'Unnamed: 0': 'Source of GHG'})
Ghg1.loc['Total'] = Ghg1.sum(numeric_only=True)
#we also created an additional row which sums the total GHG per year.
Ghg1.loc['Total', 'Source of GHG'] = 'Total GHG per Year'
Ghg1

#For the purpose of our analysis we removed the electricity generation row and focused on Industrial, Commercial, Agriculture GDP values.
CB1.drop([0], axis=0, inplace=True)
CB1.drop([4], axis=0, inplace=True)
CB1 = CB1.rename(columns={'Unnamed: 0': 'GDP Generated by Sector'})
CB1.loc['Total'] = CB1.sum(numeric_only=True)
#we also created an additional row which sums the total GDP generated each year
CB1.loc['Total', 'GDP Generated by Sector'] = 'Total GDP Per Year'
CB1 = CB1.round(2)
CB1

DF3 = Ghg1.merge(CB1, how='outer')
DF3 = DF3.transpose()
DF3 = DF3.drop(DF3.columns[[4, 5, 6]], axis=1)
DF3 = DF3.drop(labels=["GDP Generated by Sector"], axis=0, inplace=False)
DF3.columns = DF3.iloc[0]
DF3 = DF3[1:]
DF3
DF3.columns = ['Commercial/Institutional (Mt of CO2e)', 'Industrial (Mt of CO2e)','Agriculture (Mt of CO2e)','Total GHG per Year', 'Total GDP Per Year']
DF3

import pandas as pd
import numpy as np
import panel as pn
pn.extension("tabulator")

import hvplot.pandas

DF3 = DF3.reset_index()

iDF3 = DF3.interactive()

yaxis_ghg = pn.widgets.RadioButtonGroup(
    name='Y axis',
    options=['Commercial/Institutional (Mt of CO2e)', 'Industrial (Mt of CO2e)', 'Agriculture (Mt of CO2e)','Total GHG per Year','Total GDP Per Year'],
    button_type='success'
)

ghg_pipeline = (
    iDF3
    .groupby(['index'])[yaxis_ghg].mean()
    .to_frame()
    .reset_index()
    .sort_values(by='index')
    .reset_index(drop=True)
)

co2_plot = ghg_pipeline.hvplot(x = 'index', y=yaxis_ghg,line_width=4, title="GHG emissions by Sector & Totals")
ghg_bar_plot = ghg_pipeline.hvplot(kind='bar', x='index', y=yaxis_ghg, title='GHG Emissions by Usage and Total GDP')

template = pn.template.FastListTemplate(
    title='GHG Emissions across Canada',
    sidebar=[pn.pane.Markdown("# GHG Emissions and their impact on GDP")],
    main=[pn.Column(yaxis_ghg, co2_plot.panel(width=700), margin=(0,25)),
                 pn.Column(yaxis_ghg, ghg_bar_plot.panel(width=800))],
    accent_base_color="#eab676",
    header_background="#1e81b0",
)

template.show()
template.servable();

Launching server at http://localhost:57657

C:\Users\hurdg\anaconda3\envs\geo_env\Lib\site-packages\holoviews\core\data\pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  return dataset.data.dtypes[idx].type
C:\Users\hurdg\anaconda3\envs\geo_env\Lib\site-packages\holoviews\core\data\pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  return dataset.data.dtypes[idx].type
C:\Users\hurdg\anaconda3\envs\geo_env\Lib\site-packages\holoviews\core\data\pandas.py:39: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  return dataset.data.dtypes[idx].type
WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: Column(id='f3098231-a6f5-43ad-bfe4-6f651a0fd721', ...)
WARNING:bokeh.core.validation.check:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: Column(id='e190af2e-6838-47c2-b3aa-e1dd3401e0cf', ...)

G=Ghg.transpose()
G.columns = G.iloc[0]
G=G.iloc[1:]
G = G.rename(columns={'Unnamed: 0': 'Year'})
G=G.round(2 )
G

t=Ghg.iloc[[0, 1, 9, 17, 28, 29, 36]]

total=t.transpose()
total.columns = total.iloc[0]
total=total.iloc[1:]
total = total.rename(columns={'Unnamed: 0': 'Year'})

total=total.round(2 )
total.index = total.index.astype(str)
total

from pydoc import locate
from ipywidgets import Label
plt.figure(figsize=(8, 6))
plt.plot(total.index,total['Residential (Mt of CO2e)'], marker='o',label='Residential ')
plt.plot(total.index,total['Commercial/Institutional (Mt of CO2e)'], marker='o',label='Commercial/Institutional')
plt.plot(total.index,total['Industrial (Mt of CO2e)'], marker='o',label='Industrial')
plt.plot(total.index,total['Passenger Transportation (Mt of CO2e)'], marker='o',label='Passenger Transportation')
plt.plot(total.index,total['Freight Transportation (Mt of CO2e)'], marker='o',label='Freight Transportation')
plt.xlabel('Year')
plt.ylabel('Emissions (Mt of CO2e)')
plt.title('Total GHG Emissions Over the Years')
plt.legend()
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

BC=CB.transpose()
BC.columns = BC.iloc[0]
BC=BC.iloc[1:]
BC = BC.rename(columns={'Unnamed: 0': 'Year'})
BC=BC.round(2 )

plt.figure(figsize=(10, 6))

plt.plot(BC.index, BC['Industrial'], label='Industrial')
plt.plot(BC.index, BC['Commercial/Institutional'], label='Commercial/Institutional')
plt.plot(BC.index, BC['Agriculture      '], label='Agriculture')


plt.xlabel('Years')
plt.ylabel('Million')
plt.title('Total GDP Millions Over Years')
plt.legend()
plt.grid(True)
plt.show()

Freight_Transportation=G[[ 'Light Trucks - Freight',' Medium Trucks', ' Heavy Trucks',
       'Air - Freight', 'Rail - Freight', ' Marine']]
ft=Freight_Transportation.transpose()
c=ft.sum(axis=1)
plt.figure(figsize=(7, 7))
plt.pie(c,labels=c.index,wedgeprops=dict(width=0.4),autopct='%1.1f%%')
plt.title("Freight Transportation Total by Category")

Text(0.5, 1.0, 'Freight Transportation Total by Category')

Passenger_Transportation=G[[ 'Cars',
       'Light Trucks - Passenger', 'Buses', 'Air - Passenger',
       'Rail - Passenger']]
PT=Passenger_Transportation.transpose()
pt=PT.sum(axis=1)
plt.figure(figsize=(8, 8))
plt.pie(pt,autopct='%1.1f%%')
plt.title("Passenger Transportation Total by Category")
plt.legend(pt.index,loc='upper right')

<matplotlib.legend.Legend at 0x25535568750>

Industrial=G[[ 'Mining , Quarrying, and Oil and Gas Extraction', 'Pulp and Paper',
       'Iron and Steel', 'Smelting and Refining', 'Cement', 'Chemicals',
       'Petroleum Refining', 'Other Manufacturing', 'Forestry ',
       'Construction']]
I=Industrial.transpose()
i=I.sum(axis=1)
i.plot(kind='bar', stacked=True,color=(['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']))
plt.title("industrial Total by Category")
plt.ylabel('Emissions (Mt of CO2e)')

Text(0, 0.5, 'Emissions (Mt of CO2e)')

Residential=G[['Space Heating - Residential','Space Cooling - Residential','Water Heating - Residential','Lighting - Residential']
              ]
g = sns.catplot(
    data=Residential,
   kind="bar",
      alpha=.6, height=6)
g.set_xticklabels(['Space Heating ','Space Cooling ',
       'Water Heating ', 'Lighting '])

plt.ylabel('Emissions (Mt of CO2e)')
g.set_axis_labels("Sub Category", "Emissions (Mt of CO2e)")
plt.title("Residential Total by Category")

Text(0.5, 1.0, 'Residential Total by Category')

Commercial=G[[ 'Space Heating - Commercial', 'Water Heating - Commercial',
       'Auxiliary Equipment', 'Auxiliary Motors', 'Lighting - Commercial',
       'Space Cooling - Commercial', 'Street Lighting']]
sns.set_theme(style="ticks", palette="pastel")
k=sns.boxplot(
             palette=["m", "g","y","b","r"],
            data=Commercial)
k.set_xticklabels(['S-Heat', 'W-Heat ',
       'Equipment', 'Motors', 'Lighting ',
       'Cooling ', 'St-Lighting'])
sns.despine(offset=10, trim=True)

C:\Users\hurdg\AppData\Local\Temp\ipykernel_15172\2750093320.py:5: UserWarning: 
The palette list has fewer values (5) than needed (7) and will cycle, which may produce an uninterpretable plot.
  k=sns.boxplot(
C:\Users\hurdg\AppData\Local\Temp\ipykernel_15172\2750093320.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  k.set_xticklabels(['S-Heat', 'W-Heat ',

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go


# Define a list of Provinces and Territories
regions = ["AB", "BC", "MB","NB","NL","NS","NT","NU","ON","PE","QC","SK","YT"]

# Initialize an empty DataFrame to store the combined data
combined_data = pd.DataFrame()

# Loop through each Province and Territory excel file and select the total GHG column
for region in regions:
    # Define the file name based on the region code from above
    file_name = f"EN_GHG_IPCC_{region}.xlsx"
    # Read the Excel file, and select the Summary sheet
    df = pd.read_excel(file_name, sheet_name='Summary')
  # Select rows and columns containing total GHG
    df = df.loc[[3, 5], 'Unnamed: 15':]
    # Rename rows
    df = df.rename(index={3: 'Year', 5: f'ghg_{region}'})
        # Transpose the DataFrame
    df = df.transpose()
        # Combine data for this region with the combined_data DataFrame
    if combined_data.empty:
        combined_data = df
    else:
        combined_data = pd.merge(combined_data, df, on="Year", how="outer")


# Define a list of appropriate column names
column_names = ['Year','Alberta',"British Columbia","Manitoba","New Brunswick","Newfoundland & Labrador","Nova Scotia","Northwest Territories","Nunavut","Ontario", "PE Island","Quebec","Saskatchewan","Yukon"]


# Set the column names of the DataFrame
combined_data.columns = column_names



# Create a line plot
fig = px.line(
    combined_data,
    x="Year",
    y=combined_data.columns[1:],
    color_discrete_sequence=px.colors.qualitative.Light24,
    title="Figure 1b. Total Greenhouse Gas Emissions by Province and Territory in Canada (2000-2021)")


fig.update_yaxes(title_text="GHG Emissions (kt CO_2 eq)")
fig.update_yaxes(dtick=20000)
fig.update_layout(
    width=850, #Adjust the width
    height=750# Adjust the height
)
#Removing legend title
fig.update_layout(
    legend_title_text="")




fig.update_layout(
    xaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray'),
    yaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray'),
    plot_bgcolor='white',  # Set background color
    legend=dict(orientation='h', yanchor='bottom', y=-0.2, xanchor='right', x=1)
)
#Allows for zooming into axis
fig.update_xaxes(automargin=True)
fig.update_yaxes(automargin=True)

fig.show()

#extracting ghg values from dataframe combined_data
values = combined_data.loc[19, 'Alberta':'Yukon']

#Pie chart
fig = px.pie(values=values, names=values.index)

# Customize the layout
fig.update_layout(
    title='Figure 2b. Pie Chart of GHG Emissions for the Provinces and Territories of Canada ',

)

# Show the chart
fig.show()

import pandas as pd

import matplotlib.pyplot as plt

#Reading csv file
df_pop = pd.read_csv('Population.csv', encoding='latin-1').dropna(axis=0)

#Make row 0 as header
df_pop.columns = df_pop.iloc[0]

# Reset the index to exclude the first row
df_pop = df_pop[1:].reset_index(drop=True)

df_pop['Year'] = df_pop['Geography'].str.extract(r'(\d{4})')[0]
df_pop['Quarter'] = df_pop['Geography'].str.extract(r'(Q\d)')[0]

# Filter to show only Q1 for each year
df_pop = df_pop[df_pop['Quarter'] == 'Q1']

# Drop the 'Quarter' column if you no longer need it
df_pop = df_pop.drop(columns=['Quarter'])

#rest index
df_pop = df_pop[1:].reset_index(drop=True)

#sort columns by alphabebetical order
df_pop = df_pop[sorted(df_pop.columns)]
df_pop = df_pop[['Geography', 'Year', 'Canada'] + [col for col in df_pop.columns if col not in ['Geography', 'Year', 'Canada']]]


#For population data
# Extract the columns
columns_to_sort = df_pop.loc[:, 'Alberta':'Yukon']

# Sort the columns alphabetically
sorted_columns = sorted(columns_to_sort.columns)

# Reassign the sorted columns to the DataFrame
df_pops = df_pop[sorted_columns]
#Renaming the columns to match ghg columns
column_names_pop = ["Alberta", "British Columbia", "Manitoba", "New Brunswick", "Newfoundland & Labrador","Northwest Territories", "Nova Scotia", "Nunavut", "Ontario", "PE Island", "Quebec", "Saskatchewan", "Yukon"]


# Set the column names of the DataFrame
df_pops.columns = column_names_pop
row_2019_pop = df_pops.loc[20, :]


#For GHG data
# Extracting the columns
column_sorting_ghg = combined_data.loc[:, 'Alberta':'Yukon']
#Sorting the column alphabetically
sorted_columns_ghg =sorted(column_sorting_ghg.columns)


# Set the column names of the DataFrame
new_combined_data = combined_data[sorted_columns_ghg]
row_2019_ghg = new_combined_data.loc[20, :]



#Making sure data types are addressed
row_2019_pop = row_2019_pop.str.replace(',', '').astype(float)
row_2019_ghg = pd.to_numeric(row_2019_ghg, errors='coerce')

#Calculating per capita
per_capita_ghg = row_2019_ghg/row_2019_pop


#print(per_capita_ghg)
# Create a Pandas Series
per_capita_ghg = pd.Series(per_capita_ghg)



# Labeling the plot
plt.title('GHG Emissions Per Capita')
plt.xlabel('Provinces and Territories')
plt.ylabel('Emissions Per Capita')

per_capita_ghg = per_capita_ghg.sort_values(ascending=False)

# Plot a horizontal bar graph
per_capita_ghg.plot(kind='barh')

# Customize the plot
plt.title('Figure 3b. GHG Emissions Per Capita (Sorted)(2019)')
plt.xlabel('Emissions Per Capita')
plt.ylabel('Provinces and Territories')

# Show the plot
plt.show()

import pandas as pd

# Define a list of regions (e.g., Alberta, British Columbia, etc.)
regions = ["AB", "BC", "MB", "NB", "NL", "NT", "NS", "NU", "ON", "PE", "QC", "SK", "YT"]

# Initialize an empty DataFrame to store the combined data
combined_data = pd.DataFrame()


# Loop through each region
for region in regions:
    # Define the file name based on the region
    file_name = f"EN_GHG_IPCC_{region}.xlsx"

    # Check if the file exists

        # Read the Excel file
    df = pd.read_excel(file_name, sheet_name='Summary')


        # Select rows and columns containing the relevant data
    df = df.loc[[3, 6], 'Unnamed: 15':]

        # Rename rows
    df = df.rename(index={3: 'Year', 6: f'ghg_{region}'})
        # Transpose the DataFrame
    df = df.transpose()


        # Combine data for this region with the combined_data DataFrame
    if combined_data.empty:
        combined_data = df

    else:
        combined_data = pd.merge(combined_data, df, on="Year", how="outer")


# Extract row index 0 for energy sector ghg for the year 2000
energy_sec_2000 = combined_data.iloc[0]
energy_sector_2000 = energy_sec_2000[1:].tolist()
energy_sector_2000[7]=503
energy_sector_2000[5]=1487

# Extract row index 10 for energy sector ghg for the year 2010
row_10 = combined_data.iloc[10]
energy_sector_2010 = row_10[1:].tolist()

# Extract row index 19 for energy sector ghg for the year 2019
energy_sec_2019 = combined_data.iloc[19]
energy_sector_2019 = energy_sec_2019[1:].tolist()

import pandas as pd
import plotly.express as px



# Extract row index 10 to get total GHG emission data for year 2000
year_2000 = combined_data.iloc[0]
total_ghg_2000 = year_2000[1:].tolist()
total_ghg_2000[7]=528
total_ghg_2000[5]=1530

# Extract row index 10 to get total GHG emission data for year 2010
year_2010 = combined_data.iloc[10]
total_ghg_2010 = year_2010[1:].tolist()

# Extract row index 19 to get total GHG emission data for the year 2019
year_2019 = combined_data.iloc[19]
total_ghg_2019 = year_2019[1:].tolist()




#2000 dataframe
pop_2000 = df_pop.loc[0, 'Alberta':'Yukon'].tolist()

data = {
    'Province': ["AB", "BC", "MB", "NB", "NL", "NT", "NS", "NU", "ON", "PE", "QC", "SK", "YT"],
    'Total_GHG_Emissions': total_ghg_2000,
    'Population': pop_2000,
    'Bubble_Size': energy_sector_2000
}

#2010 dataframe
df_2000 = pd.DataFrame(data)
pop_2010 = df_pop.loc[9, 'Alberta':'Yukon'].tolist()
data = {
    'Province': ["AB", "BC", "MB", "NB", "NL", "NT", "NS", "NU", "ON", "PE", "QC", "SK", "YT"],
    'Total_GHG_Emissions': total_ghg_2010,
    'Population': pop_2010,
    'Bubble_Size': energy_sector_2010
}

df_2010 = pd.DataFrame(data)

#2019 dataframe
pop_2019 = df_pop.loc[19, 'Alberta':'Yukon'].tolist()

data = {
    'Province': ["AB", "BC", "MB", "NB", "NL", "NT", "NS", "NU", "ON", "PE", "QC", "SK", "YT"],
    'Total_GHG_Emissions': total_ghg_2019,
    'Population': pop_2019,
    'Bubble_Size': energy_sector_2019
}

df_2019 = pd.DataFrame(data)


# Add a column to distinguish the data frames

df_2010['Year'] = '2010'
df_2000['Year'] = '2000'
df_2019['Year'] = '2019'

# Combine the two data frames
combined_df = pd.concat([df_2019, df_2010,df_2000], ignore_index=True)

# Create a scatter plot using Plotly Express
fig = px.scatter(
    combined_df,
    x='Population',
    y='Total_GHG_Emissions',
    size='Bubble_Size',
    color='Year',
    text ='Province',
    hover_name='Province',
    size_max=50

)


fig.update_layout(
    xaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray'),
    yaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray'),
    plot_bgcolor='white',  #

)



# Set axis titles
fig.update_xaxes(title_text='Population')
fig.update_xaxes(type='linear')
fig.update_yaxes(title_text='Total GHG Emissions (kt CO_2 eq)')

# Set the title
fig.update_layout(title='Figure 4b. Comparison of GHG Emissions and Population by Province/Territory (2000 vs. 2010 vs. 2019)')

# Enable zoom and pan options
fig.update_xaxes(automargin=True)
fig.update_yaxes(automargin=True)


# Show the plot
fig.show()

#### The below code re-runs the intial data import and manipulation code to re-create the original GHG dataframes ##
# Define a list of Provinces and Territories 
regions = ["AB", "BC", "MB","NB","NL","NS","NT","NU","ON","PE","QC","SK","YT"]  

# Initialize an empty DataFrame to store the combined data
combined_data_copy = pd.DataFrame()

# Loop through each Province and Territory excel file and select the total GHG column 
for region in regions:
    # Define the file name based on the region code from above
    file_name = f"EN_GHG_IPCC_{region}.xlsx"    
    # Read the Excel file, and select the Summary sheet 
    df = pd.read_excel(file_name, sheet_name='Summary')
  # Select rows and columns containing total GHG 
    df = df.loc[[3, 5], 'Unnamed: 15':]
    # Rename rows
    df = df.rename(index={3: 'Year', 5: f'ghg_{region}'})
        # Transpose the DataFrame
    df = df.transpose()
        # Combine data for this region with the combined_data DataFrame
    if combined_data_copy.empty:
        combined_data_copy = df
    else:
        combined_data_copy = pd.merge(combined_data_copy, df, on="Year", how="outer")
        

# Define a list of appropriate column names
column_names = ['Year','Alberta',"British Columbia","Manitoba","New Brunswick","Newfoundland & Labrador","Nova Scotia","Northwest Territories","Nunavut","Ontario", "PE Island","Quebec","Saskatchewan","Yukon"]
    

# Set the column names of the DataFrame
combined_data_copy.columns = column_names

#Ammend the original population dataframes
#Reformat df_pops dataframe to incclude year - specify new dataframe name "df_pop_wUear"
sorted_columns.append('Year')
df_pops_wYear = df_pop[sorted_columns]
column_names_pop_wYear = ["Alberta", "British Columbia", "Manitoba", "New Brunswick", "Newfoundland & Labrador","Northwest Territories", "Nova Scotia", "Nunavut", "Ontario", "PE Island", "Quebec", "Saskatchewan", "Yukon", "Year"]
df_pops_wYear.columns = column_names_pop_wYear

#Melt regional GHG dataframe into 'long' format - required for mapping function
regionGHG_long = pd.melt(combined_data_copy, id_vars=['Year'], var_name='Province')
regionGHG_long.rename(columns={'value': 'GHG'}, inplace=True)
regionGHG_long['GHG'].fillna(0, inplace=True)
regionGHG_long[['Year','GHG']] = regionGHG_long[['Year','GHG']].astype(int)
regionGHG_long['Province'] = regionGHG_long['Province'].astype(str)

#Similarly melt the population dataframe
regionPop_long = pd.melt(df_pops_wYear, id_vars=['Year'], var_name='Province')
regionPop_long.rename(columns={'value': 'Population'}, inplace=True)
regionPop_long.replace(',','', regex=True, inplace=True)
regionPop_long[['Year','Population']] = regionPop_long[['Year','Population']].astype(int)
regionPop_long['Province'] = regionPop_long['Province'].astype(str)


#Merge the population and GHG dataframes and create a per-capita variable - multiply by 1000 to simplify units 
regionPerCap_long = regionPop_long.merge(regionGHG_long, on=['Year','Province'])
regionPerCap_long['ghg_percapita'] = regionPerCap_long['GHG']/regionPerCap_long['Population'] *1000

#Import shapefile of provincial/territorial geometries
#Format the dataframe to mesh with the per-capita dataframe - specify function to remove unwanted characters from province column
import geopandas as gpd
geo_df = gpd.read_file("C:/Users/hurdg/OneDrive/Documents/MDSA/DATA 601/Data 601 - Project/Datasets/georef-canada-province-millesime.shp")     
geo_df.rename(columns={'prov_name_e':'Province'}, inplace=True)
geo_df = geo_df.loc[:, ['Province', 'geometry']]

def remove_unwanted(x):
    x = x.replace("]", "")
    x = x.replace("[", "")
    return x.translate(str.maketrans({"'":None}))


geo_df['Province'] = geo_df['Province'].apply(remove_unwanted)

#Merge per-capita dataframe with geometries
geo_df = geo_df.merge(regionPerCap_long, on = 'Province')

#Create json using 2010 data (arbitrary) to act as geospatial reference for choropleth
gdf_2010 = geo_df[geo_df['Year'] == 2010]
geo_df['Year'] = geo_df['Year'].astype(str)
json_2010 = gdf_2010.to_json()
import json
json_2010 = json.loads(json_2010)


#Create choropleth within plotly
fig = px.choropleth(
    geo_df,
    locations="Province",
    featureidkey="properties.Province", ### ! changed !
    geojson=json_2010,
    color=geo_df.ghg_percapita.astype(float),
    hover_name="Province",
    range_color=(0, 100),
    color_continuous_scale='YlOrBr',
    animation_frame=geo_df.Year.astype(str)
)

#Format slider bar label and font
sliders = [dict(
currentvalue={"prefix": "<b>Year </b>"},
font=dict(size=20,color="Black"))]
fig.update_layout(sliders=sliders)

#Specify figure title
fig.update_layout(
    title_text = 'Figure 5b.Per Capita GHG Emissions',
    #Specify colorbar to appear on left side of map
    coloraxis_colorbar_x=-0.1)

#Snap initial figure frame around map
fig.update_geos(fitbounds="locations", visible=False)

fig.show()

	Source of GHG	2000	2001	2002	2003	2004	2005	2006	2007	2008	...	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
1	Industrial	407435.20	404893.90	414638.90	419498.50	433942.00	445641.20	449969.50	448495.90	437702.20	...	437861.50	447856.50	459845.50	479792.90	473645.10	464860.20	486485.20	503277.60	501449.5	467445.0
2	Commercial/Institutional	823297.00	849552.00	882345.00	903023.00	932062.00	962288.00	1000647.00	1032522.00	1051936.00	...	1105968.00	1126960.00	1152136.00	1179705.00	1196301.00	1218749.00	1252804.00	1289520.00	1323236.0	1273834.0
3	Agriculture	21276.05	21233.06	20139.70	22995.43	25620.29	25919.98	25881.79	24518.72	26710.91	...	25804.04	26274.75	31980.63	28653.57	30322.48	32602.95	33461.52	33133.78	35179.4	37025.0
4	Electricity Generation	26218.92	25645.92	26979.08	27557.83	27696.92	29514.00	29096.17	30849.00	32367.00	...	31475.92	30627.00	30962.00	31990.00	32397.00	33540.00	33989.00	34544.00	34824.0	34176.0

	Source of GHG	2000	2001	2002	2003	2004	2005	2006	2007	2008	...	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
9	Commercial/Institutional (Mt of CO2e)	55.105833	56.536416	59.064334	62.027890	59.021386	55.983739	52.163896	54.511811	53.391508	...	50.091385	46.805387	47.566451	49.606041	49.399903	49.398433	52.325620	52.259235	54.232999	50.498562
17	Industrial (Mt of CO2e)	160.355409	158.726351	159.201689	165.741093	168.367031	164.390842	164.723550	174.256674	165.301070	...	168.166005	168.377670	166.845965	167.363194	171.675708	166.879338	171.757890	179.198073	181.695153	165.606838
44	Agriculture (Mt of CO2e)	15.602440	15.001209	14.327296	14.981179	14.866488	15.129819	15.242616	15.702339	15.764766	...	16.938284	16.471504	17.153562	17.757157	18.346355	18.554234	18.988716	19.568804	18.843595	17.999334
Total	Total GHG per Year	231.063683	230.263976	232.593319	242.750162	242.254905	235.504400	232.130062	244.470823	234.457344	...	235.195673	231.654562	231.565977	234.726392	239.421967	234.832005	243.072227	251.026112	254.771747	234.104735

	GDP Generated by Sector	2000	2001	2002	2003	2004	2005	2006	2007	2008	...	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
1	Industrial	407435.20	404893.90	414638.9	419498.50	433942.00	445641.20	449969.50	448495.90	437702.20	...	437861.50	447856.50	459845.50	479792.90	473645.10	464860.20	486485.20	503277.60	501449.5	467445.0
2	Commercial/Institutional	823297.00	849552.00	882345.0	903023.00	932062.00	962288.00	1000647.00	1032522.00	1051936.00	...	1105968.00	1126960.00	1152136.00	1179705.00	1196301.00	1218749.00	1252804.00	1289520.00	1323236.0	1273834.0
3	Agriculture	21276.05	21233.06	20139.7	22995.43	25620.29	25919.98	25881.79	24518.72	26710.91	...	25804.04	26274.75	31980.63	28653.57	30322.48	32602.95	33461.52	33133.78	35179.4	37025.0
Total	Total GDP Per Year	1252008.25	1275678.96	1317123.6	1345516.93	1391624.29	1433849.18	1476498.29	1505536.62	1516349.11	...	1569633.54	1601091.25	1643962.13	1688151.47	1700268.58	1716212.15	1772750.72	1825931.38	1859864.9	1778304.0

	Commercial/Institutional (Mt of CO2e)	Industrial (Mt of CO2e)	Agriculture (Mt of CO2e)	Total GHG per Year	Total GDP Per Year
2000	55.105833	160.355409	15.60244	231.063683	1252008.25
2001	56.536416	158.726351	15.001209	230.263976	1275678.96
2002	59.064334	159.201689	14.327296	232.593319	1317123.6
2003	62.02789	165.741093	14.981179	242.750162	1345516.93
2004	59.021386	168.367031	14.866488	242.254905	1391624.29
2005	55.983739	164.390842	15.129819	235.5044	1433849.18
2006	52.163896	164.72355	15.242616	232.130062	1476498.29
2007	54.511811	174.256674	15.702339	244.470823	1505536.62
2008	53.391508	165.30107	15.764766	234.457344	1516349.11
2009	50.680115	153.227149	13.307905	217.215169	1468674.79
2010	50.474459	162.094963	15.63274	228.202162	1520466.55
2011	50.091385	168.166005	16.938284	235.195673	1569633.54
2012	46.805387	168.37767	16.471504	231.654562	1601091.25
2013	47.566451	166.845965	17.153562	231.565977	1643962.13
2014	49.606041	167.363194	17.757157	234.726392	1688151.47
2015	49.399903	171.675708	18.346355	239.421967	1700268.58
2016	49.398433	166.879338	18.554234	234.832005	1716212.15
2017	52.32562	171.75789	18.988716	243.072227	1772750.72
2018	52.259235	179.198073	19.568804	251.026112	1825931.38
2019	54.232999	181.695153	18.843595	254.771747	1859864.9
2020	50.498562	165.606838	17.999334	234.104735	1778304.0

Source of GHG	Total GHG Emissions Including Electricity (Mt of CO2e)	Residential (Mt of CO2e)	Space Heating - Residential	Water Heating - Residential	Appliances	Major Appliances	Other Appliances	Lighting - Residential	Space Cooling - Residential	Commercial/Institutional (Mt of CO2e)	...	Rail - Passenger	Freight Transportation (Mt of CO2e)	Light Trucks - Freight	Medium Trucks	Heavy Trucks	Air - Freight	Rail - Freight	Marine	Off-Road (Mt of CO2e)	Agriculture (Mt of CO2e)
2000	465.71	74.53	46.18	13.84	10.46	7.8	2.66	3.28	0.78	55.11	...	0.23	64.81	10.24	10.77	28.97	0.56	6.36	7.92	5.89	15.6
2001	463.93	74.14	43.3	14.32	11.49	8.43	3.06	3.66	1.39	56.54	...	0.23	65.24	10.38	12.35	27.18	0.47	6.31	8.55	6.56	15.0
2002	469.76	75.76	45.46	14.22	10.95	7.88	3.07	3.49	1.63	59.06	...	0.21	64.0	10.77	11.94	26.84	0.51	5.74	8.2	6.73	14.33
2003	487.54	78.59	47.5	14.58	11.47	8.11	3.36	3.73	1.31	62.03	...	0.2	68.47	10.93	14.04	28.93	0.47	5.81	8.3	6.81	14.98
2004	491.29	77.22	47.2	14.39	11.1	7.7	3.39	3.57	0.96	59.02	...	0.2	72.57	11.1	15.58	30.23	0.5	5.98	9.18	6.97	14.87
2005	485.46	75.06	45.34	14.23	10.38	7.1	3.27	3.28	1.84	55.98	...	0.21	74.26	11.25	14.53	32.15	0.54	6.37	9.41	7.12	15.13
2006	476.56	71.5	42.57	14.06	10.27	6.91	3.36	3.18	1.41	52.16	...	0.21	74.44	11.08	16.81	31.08	0.5	6.68	8.29	7.21	15.24
2007	502.37	78.11	48.39	14.5	10.54	6.97	3.58	3.19	1.48	54.51	...	0.22	78.3	11.72	17.28	32.48	0.41	7.16	9.25	7.31	15.7
2008	488.5	75.67	47.83	13.84	9.98	6.45	3.52	3.0	1.02	53.39	...	0.25	79.4	11.61	18.22	32.68	0.34	7.54	9.0	7.39	15.76
2009	465.27	71.51	45.72	13.03	9.23	5.85	3.38	2.76	0.77	50.68	...	0.23	78.8	11.77	19.44	32.15	0.31	6.42	8.71	7.35	13.31
2010	479.88	69.64	42.7	13.03	9.53	5.86	3.67	2.85	1.53	50.47	...	0.19	82.93	12.15	21.8	33.26	0.37	6.34	9.02	7.4	15.63
2011	486.93	69.93	44.44	13.17	8.49	5.13	3.36	2.48	1.35	50.09	...	0.22	83.23	12.16	21.36	34.96	0.38	7.14	7.23	7.52	16.94
2012	479.66	64.79	40.23	12.65	8.17	4.82	3.35	2.33	1.41	46.81	...	0.19	83.34	12.45	21.15	35.03	0.42	7.35	6.95	7.62	16.47
2013	484.53	66.42	42.93	12.36	7.98	4.62	3.35	2.21	0.94	47.57	...	0.16	84.53	12.96	22.13	35.36	0.44	7.09	6.55	7.76	17.15
2014	484.33	66.36	43.75	11.77	7.86	4.48	3.38	2.14	0.84	49.61	...	0.15	83.68	12.98	21.54	35.57	0.43	7.28	5.88	7.93	17.76
2015	487.94	66.3	42.75	12.12	8.14	4.58	3.56	2.17	1.12	49.4	...	0.16	80.45	13.55	20.62	33.6	0.39	6.92	5.36	8.13	18.35
2016	477.75	61.05	39.03	11.39	7.39	4.12	3.27	1.93	1.32	49.4	...	0.16	77.47	14.54	20.03	31.43	0.41	6.35	4.71	8.33	18.55
2017	493.83	63.06	41.09	11.87	7.26	4.02	3.25	1.87	0.97	52.33	...	0.18	81.71	15.07	20.55	33.24	0.49	7.27	5.09	8.46	18.99
2018	511.12	65.04	43.35	12.17	6.67	3.67	2.99	1.67	1.19	52.26	...	0.17	85.34	15.68	21.64	34.76	0.55	7.43	5.28	8.64	19.57
2019	513.54	61.5	41.18	11.26	6.63	3.59	3.04	1.63	0.79	54.23	...	0.18	86.28	16.21	21.29	34.73	0.53	7.48	6.04	8.77	18.84
2020	454.5	57.1	36.79	11.4	6.32	3.41	2.9	1.55	1.05	50.5	...	0.09	78.18	14.01	18.73	32.34	0.65	7.06	5.39	8.82	18.0

Exploratory Data Analysis of Greenhouse Gas Emissions in Canada¶

Introduction¶

Data Set¶

Guiding Questions¶

Data Wrangling and Visualization for Guiding Question 1¶

Data Wrangling and Visualization for Guiding Question 2¶

Findings and Analysis of Guiding Question 2¶

Conclusion¶

Reference List¶